max_seq_len: int = 512, 最大总序列长度(以token计算),就是需要放到KVcache里的总长度 max_batch_size: int = 8, max_gen_len: 表示生成的文本的最大长度。如果未指定,那么将使用模型参数中的最大序列长度减1。 编辑于 2024-06-28 19:55・IP 属地北京 ...
T_max = get_T_max(len(train_dataset), args.batch_size, args.max_epochs, True) work_dir = get_work_dir(f'runs/{args.model_type}') config = Config({ 'train': { 'dataloader': { 'batch_size_per_gpu': args.batch_size, 'workers_per_gpu': 1, 'shuffle': True, 'drop_last': ...
top_p:float=0.9,max_seq_len:int=128,max_gen_len:int=64,max_batch_size:int=4,):generator=Llama.build(ckpt_dir=ckpt_dir,tokenizer_path=tokenizer_path,max_seq_len=max_seq_len,max_batch_size=max_batch_size,)prompts=[# For these prompts, the expected answer is the natural continuation o...
max_batch_size=max_batch_size,)prompts=["上下五千年,英雄万万千。黄沙百战穿金甲,不破楼兰终不还",]results=generator.text_completion(prompts,max_gen_len=max_gen_len,temperature=temperature,top_p=top_p,)
per_device_train_batch_size=6if use_flash_attention else4, gradient_accumulation_steps=2, gradient_checkpointing=True, optim="paged_adamw_32bit", logging_steps=10, save_strategy="epoch", learning_rate=2e-4, bf16=True, tf32=True, max_grad_norm=0.3, warm...
# batch size per device during traininggradient_accumulation_steps=2,# number of steps before ...
layer, seq_len=position_ids.max() + 1) # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head] query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer, cos, sin, position_ids)query_layer的维度为：[seq, batch, heads, head_dim]key_layer...
an A100)fp16 = Falsebf16 = True# Batch size per GPU for trainingper_device_train_batch_size = 4# Number of update steps to accumulate the gradients forgradient_accumulation_steps = 1# Enable gradient checkpointinggradient_checkpointing = True# Maximum gradient normal (gradient clipping)max_...
weight_decay=0.0),accumulative_counts=ORI_BATCH_SIZE/args.batch_size)scheduler_cfgs=[dict(type=StepLR,step_size=1,gamma=0.85)]model,optimizer,schedulers=strategy.prepare(model,optim_wrapper=optim_cfg,param_scheduler=scheduler_cfgs,dispatch_kwargs=dict(max_iters=max_iters,max_epochs=args.max_...
:param x: 待归一化的tensor,通常为模型某一层的输出,形状为 (batch_size, ..., dim) ''' returnx * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) # 前向传播 # 返回经过标度的归一化张量作为RMSNorm层的输出 defforward(self, x): ...